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Compute the critical value for the KBQD k-sample tests

Source: R/critical_value.R
cv_ksample.Rd

This function computes the empirical critical value for the k-sample KBQD tests using the centered Gaussian kernel, with bootstrap, permutation, or subsampling.

Usage

cv_ksample(
  x,
  y,
  h,
  B = 150,
  b = 0.9,
  Quantile = 0.95,
  method = "subsampling",
  compute_variance = TRUE
)

Arguments

x

matrix containing the observations to be used in the k-sample test

y

vector indicating the sample for each observation

h

the tuning parameter for the test using the Gaussian kernel

B

the number of bootstrap/permutation/subsampling samples to generate

b

the subsampling block size (only used if method is "subsampling")

Quantile

the quantile of the bootstrap/permutation/subsampling distribution to use as the critical value

method

the method to use for computing the critical value (one of "bootstrap", "permutation")

compute_variance

indicates if the nonparametric variance is computed. Default is TRUE.

Value

a vector of two critical values corresponding to different formulation of the k-sample test statistics.